E15test3
Date: 08.04.21
We use WGCNA to run an iterative analysis on a data set. If no features are prided, HVGs are calculated in the script in the script, using Seurat, the mvp method a threshold of 0.25 for the dispersion.
Pre-analysis
We need to first set up our working environment.
my.date = format(Sys.Date(), "%d.%m.%y")
# The following setting is important, do not omit
backup.options = options()
options(stringsAsFactors = FALSE)
Parse all the parameters and data we need to be able to run the analysis. We need the data in seurat format. This must contain at least 20 samples (no problems with sc data), according to the documentation of the package itself.
# placeholder for the variables and options we will get from an upper level pipeline
project_name = params$project.name
p.Wdata = params$sc.data
gnames = params$gene.names
gnames[,2]= as.character(gnames[,2])
rownames(gnames) = gnames[,1]
Wdata = params$data
# The subset in form of cell identities, F if using the whole sample
my.subset = params$cells
# Variable genes, or genes to use, F if the genes will be calculated in the script
my.vargenes = params$features
my.dr = params$reduction
dir.create(paste0(params$dir,"./WGCNA_results"), showWarnings = F)
my.sp = params$sp
is.pseudocell = params$is.pseudocell
my.min.cell = params$min.cell
Now we calculate the genes we will use.
if (params$GO==T) {
if (my.vargenes == F){
my.gouni = rownames(p.Wdata@assays$RNA@counts)[which(apply(p.Wdata@assays$RNA@counts, 1, function(x) length(which(x >0))) > my.min.cell-1)]
} else {
my.gouni = rownames(p.Wdata@assays$RNA@counts)[which(apply(p.Wdata@assays$RNA@counts, 1, function(x) length(which(x >0))) > 0)]
}
}
# If we need to subset the data
if (my.subset) {
p.Wdata = subset(p.Wdata, idents=my.subset)
}
# If no variable genes are provided
nonex = which(apply(p.Wdata@assays$RNA@counts, 1, function(x) length(which(x >0))) < my.min.cell)
if (my.vargenes == F) {
# First get rid of non-expressed genes
p.Wdata = subset(p.Wdata, features = rownames(p.Wdata@assays$RNA)[-nonex])
# Find the variable genes
p.Wdata=Seurat::FindVariableFeatures(p.Wdata, dispersion.cutoff = c(0.25,Inf), mean.cutoff = c(0,Inf), selection.method = "mvp", assay = "RNA")
Seurat::VariableFeaturePlot(p.Wdata, assay = "RNA")
Expr = Seurat::VariableFeatures(object = p.Wdata, assay = "RNA")
} else { Expr = my.vargenes }
# if (length(my.ortho) > 1) {
# Expr = Expr[which(Expr %in% my.ortho[,1])]
# Expr = Expr[ which(Expr %in% rownames(p.Wdata@assays$RNA)[-nonex]) ]
# }
if (is.pseudocell) {
datExpr=Wdata@assays$RNA@counts[Expr,]
# datExpr = datExpr[which(rowSums(as.matrix(datExpr))>0),]
# Expr = rownames(datExpr)
} else{datExpr=Wdata@assays$RNA@data[Expr,]}
# Check the length
print(paste0("We have ", length(Expr), " genes in the variable genes object"))
## [1] "We have 2967 genes in the variable genes object"
# Check the size and transform
dim(datExpr)
## [1] 2967 513
datExpr = t(as.matrix(datExpr))
Now we need to calculate the soft threshold power. First it calculates the similarity and then transforms this similarity to a weighted network. The scale-free topology is calculated for each of the powers.
We choose the smallest power for which the scale-free topology fit index reaches 0.90. If none of the powers reaches 0.90, we take the one with the maximum, as long as we have a number above 0.75. If none of them reaches at least 0.75 we need to check our dataset.
## Warning: executing %dopar% sequentially: no parallel backend registered
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'y'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.000237 -0.608 0.610 1500.000 1.50e+03 1600.0
## 2 2 0.255000 -12.000 0.948 769.000 7.67e+02 895.0
## 3 3 0.147000 -4.970 0.812 400.000 3.95e+02 513.0
## 4 4 0.371000 -4.380 0.503 212.000 2.05e+02 310.0
## 5 5 0.913000 -5.200 0.889 115.000 1.09e+02 210.0
## 6 6 0.953000 -4.190 0.944 63.800 5.83e+01 154.0
## 7 7 0.969000 -3.380 0.965 36.700 3.16e+01 120.0
## 8 8 0.972000 -2.800 0.968 22.100 1.73e+01 98.0
## 9 9 0.972000 -2.400 0.964 13.900 9.64e+00 83.0
## 10 10 0.958000 -2.150 0.949 9.190 5.45e+00 72.0
## 11 12 0.946000 -1.800 0.946 4.670 1.82e+00 56.8
## 12 14 0.953000 -1.590 0.967 2.810 6.35e-01 46.4
## 13 16 0.952000 -1.470 0.970 1.890 2.31e-01 38.7
## 14 18 0.964000 -1.420 0.984 1.360 8.98e-02 32.8
## 15 20 0.956000 -1.400 0.976 1.030 3.64e-02 28.1
## 16 22 0.938000 -1.380 0.947 0.800 1.54e-02 24.2
## 17 24 0.921000 -1.370 0.924 0.637 6.57e-03 21.1
## 18 26 0.946000 -1.340 0.961 0.516 2.89e-03 18.4
## 19 28 0.962000 -1.320 0.985 0.425 1.31e-03 16.2
## 20 30 0.907000 -1.340 0.930 0.354 6.09e-04 14.3
## [1] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## [1] "Or power is 5"
WGCNA analysis
Now, the following piece of code will run WGCNA iteratively, to end up with out final modules. The iterations follow these steps:
- Tree cutting for module calculation
- Calculate an adjacency matrx from the data, then turn it into topological overlap and then into a distance matrix
- Calculate the tree based on the topological overlap distance
- Calculate the automatic height to cut out the 0.05 quantile
- Generate a matrix where we calculate the amount of modules, size of de modules and wheter if we have a grey module based on:
- Different minimum module sizes, arbitrarely set to 7:30
- Different cut-heiughts going 0.0005 up and down from the automatic height in steps of 0.0001
- Check if any combination of the parameters will get rid of the grey module
- If we only get grey modules, subset the matrix for the height at which the grey module is the smallest
- If we have a combination without grey module AND we have at least the same number of modules as in the beginning, we subset for whichever those heights are
- Take wichever min module sizes gives us at least the same amount of clusters as in the beginning, if none, then the highest
- Chose the max of the reminding min module sizes.
- Calculate the actual modules
- Calculate the eigengenes
- Calculate the module membership per gene
- Calculate the p.value of the membership to a given module
- Get rid of the genes in the grey module
- Delete the genes that are not significantely associated with their module
- Save the remaining geneset
- Print how many genes were deleted due to significance
- Unless 0 genes were deleted in the last step, update the expression matrices and beginn again.
ONLY THE FIRST TWO ITERATIONS ARE DIFFERENT.
FIRST:
- During the tree cutting
- Set the minimum module size to arbitrary 15
- Subset for the heights that get rid of at least 50% of the genes with that min module size
- Choose the height that gives us the most clusters
SECOND:
- Set the resulting number of modules as the ground number of modules
# Running WGCNA iteratively
my.Clnumber = 20
change = 0
genesets=list()
nonsig = 1
while(nonsig != 0) {
my.adjacency =WGCNA::adjacency(datExpr,type = "signed", power = my.power, corFnc = "bicor")
# Turn adjacency into topological overlap (high overlap if they share the same "neighborhood")
TOM=WGCNA::TOMsimilarityFromExpr(datExpr,networkType = "signed", TOMType = "signed", power = my.power, corType = "bicor")
#Put the names in the tree
colnames(TOM) <- gnames[colnames(datExpr),2]
rownames(TOM) <- gnames[colnames(datExpr),2]
#Make it a distance
dissTOM = 1-TOM
# Call the hierarchical clustering function
geneTree = hclust(as.dist(dissTOM), method = "average")
# Here I calculate the cutting height. Using the same formula and approach that the WGCNA package uses for the automatic function
nMerge = length(geneTree$height) # The whole height of the tree
refQuantile = 0.05 # What's the quantile that we want to exclude
refMerge = round(nMerge * refQuantile)
refHeight = geneTree$height[refMerge]
cutheight = signif(0.99 * (max(geneTree$height) - refHeight) + refHeight,4)
# We construct THE TABLE that will help us make decisions
# Min cluster sizes, from 7 to 30
x=seq(7,30,1)
# The height, up and down from the calculated height. We expect some "No module detected"
y=seq(cutheight-0.0005,cutheight + 0.0005,0.0001)
# The actual dataframe
w=data.frame()
# Populate, with i=min cluster size, j=cutting height, z=total number of clusters, z.1.=what's the first cluster? 0 is grey 1 is something else,
# z.1.'=what's the size of the first cluster?
for (i in x) {
for (j in y) {
sink("aux")
z=table(dynamicTreeCut::cutreeDynamic(dendro = geneTree, method="tree", minClusterSize = i, deepSplit = T, cutHeight = j, verbose = 0))
sink(NULL)
v=data.frame(i,j,dim(z),names(z[1]),unname(z[1]))
w=rbind(w,v)
}
}
# The height is then the one where we have the least number of genes in the first cluster
my.height = w$j[which(w$unname.z.1..==min(w$unname.z.1..))]
# Since different heights can give us the minimum grey size, we chose the computed height, if present, or the highest one.
if (cutheight %in% my.height) {
my.Clsize = w[which(w$j == cutheight),]
} else { my.Clsize = w[which(w$j == max(my.height)),] }
# This is to know, if we're looking for a minimum of cluster numbers
# If we still have a lot of genes, we don't want to limit the number of clusters
# if ( ((dim(datExpr)[2]) / length(Expr)) > 0.6 ) {change = 0}
# If this is the first iteration after 0.6 of the genes are gone, we assign the number of clusters (and an extra for the grey in the case)
if (change == 2) {
my.Clnumber = length(table(dynamicColors)) + 1
}
# Count another iteration
change = change + 1
# If we don't have a gray cluster anymore, then we subset for those rows, and set a new height. ONLY if we get the same amount of clusters!
if (any(w$names.z.1.. == 1)) { #any combination gives us no grey
my.Clsize = w[which(w$names.z.1.. == 1),] # Take all combinations that gives us no grey
if (any(my.Clsize$dim.z. >= (my.Clnumber -1) )) { # If there is any giving us the determined amount or more
my.Clsize = my.Clsize[which(my.Clsize$dim.z. >= (my.Clnumber - 1) ),,drop=F] # Subset for those
} else { my.Clsize = my.Clsize[which(my.Clsize$dim.z. == max(my.Clsize$dim.z.)),,drop=F] } # Or for the highest
# Take the ones with the smallest number of clusters
my.Clsize = my.Clsize[which(my.Clsize$dim.z. == min(my.Clsize$dim.z.)),,drop=F]
# Take the one with the highest min cluster size
my.Clsize = my.Clsize[which(my.Clsize$i == max(my.Clsize$i)),,drop=F]
if (cutheight %in% my.Clsize$j) { # if original computed height is in,
my.height = cutheight # take it
} else { my.height = max(my.Clsize$j) } # Otherwise, the highest height
my.Clsize = max(my.Clsize$i)
}
# Subset the table again, for those sizes that will gives the same number of clusters or more. IF NONE, use the highest number
if (!any(w$names.z.1.. == 1)){
if (any(my.Clsize$dim.z. >= my.Clnumber)) {
my.Clsize = my.Clsize[which(my.Clsize$dim.z. >= my.Clnumber),,drop=F]
} else {
my.Clsize = my.Clsize[which(my.Clsize$dim.z. == max(my.Clsize$dim.z.)),,drop=F]}
# Take the ones with the smallest number of clusters
my.Clsize = my.Clsize[which(my.Clsize$dim.z. == min(my.Clsize$dim.z.)),,drop=F]
# Take the one with the highest min cluster size
my.Clsize = my.Clsize[which(my.Clsize$i == max(my.Clsize$i)),,drop=F]
if (cutheight %in% my.Clsize$j) { # if original computed height is in,
my.height = cutheight # take it
} else { my.height = max(my.Clsize$j) } # Otherwise, the highest height
my.Clsize = max(my.Clsize$i)
}
# If we still have more than 60% of the genes, we just use the min size of 15 regardles
if ( change < 3 ) {
my.Clsize = 15
my.height = cutheight
}
dynamicMods = dynamicTreeCut::cutreeDynamic(dendro = geneTree, method="tree", minClusterSize = my.Clsize, deepSplit = T, cutHeight = my.height)
#dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM, deepSplit = 2, minClusterSize = minModuleSize)
table(dynamicMods)
# Convert numeric lables into colors
dynamicColors = WGCNA::labels2colors(dynamicMods)
table(dynamicColors)
# Plot the dendrogram and colors underneath
WGCNA::plotDendroAndColors(geneTree, dynamicColors, "Modules",
dendroLabels = NULL,
cex.dendroLabels = 0.6,
addGuide = TRUE,
main = "Gene dendrogram and module colors",
guideAll = F)
par(mfrow=c(1,1))
# Calculate eigengenes
MEList = WGCNA::moduleEigengenes(as.matrix(datExpr), colors = dynamicColors)
MEs = MEList$eigengenes
# Calculate the module membership
geneModuleMembership = as.data.frame(WGCNA::signedKME(datExpr, MEs))
MMPvalue = as.data.frame(WGCNA::corPvalueStudent(as.matrix(geneModuleMembership), nrow(datExpr)))
# We're gonna make a list, where we keep the genes that are significantly associated with each module
x=c()
xy=list()
# We also need a vector with all the dynamic colors
dcols = 1:length(levels(as.factor(dynamicColors)))
# Getting rid of the grey module
grey.genes = length(which(dynamicColors == "grey"))
if (any(levels(as.factor(dynamicColors)) == "grey")) {
dcols = dcols[-which(levels(as.factor(dynamicColors)) == "grey")]
}
# Run the loop to get the genes
for (i in dcols) {
modGenes = rownames(MMPvalue)[which(dynamicColors==levels(as.factor(dynamicColors))[i] & MMPvalue[,i]<0.01)]
x=c(x,modGenes)
xy[[i]]=modGenes
#print(paste0(levels(as.factor(dynamicColors))[i]," ",length(modGenes),
#" of ", length(which(dynamicColors==levels(as.factor(dynamicColors))[i]))))
#print(gnames[modGenes,2])
}
# Make a new list, where we keep ALL the gens thar are left from the iteration, that will be used to make the new object. To keep track
genesets[[length(genesets)+1]] = colnames(datExpr)
# Give me a message saying how many genes are gone this time
cat( paste0( grey.genes, " genes not assigned to any module.", '\n',
length(which(!(colnames(datExpr)%in%x))) - grey.genes, " genes excluded due to significance."))
# Save this also, cause if it's 0 then we stop the whole thing
nonsig = length(which(!(colnames(datExpr)%in%x)))
# If it ain't 0, subset the dynamic colors and the expression data
if (length(which(!(colnames(datExpr)%in%x))) != 0) {
dynamicColors=dynamicColors[-which(!(colnames(datExpr)%in%x))]
datExpr=datExpr[,-(which(!(colnames(datExpr)%in%x)))]
}
}
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 528 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 135 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 161 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 37 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 36 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 9 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 8 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 3 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 2 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 1 genes excluded due to significance.
## Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
## "all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used
## for individual columns with zero (or missing) MAD.
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## 0 genes not assigned to any module.
## 0 genes excluded due to significance.
We calculate here the expression of genes using the single-cell data
p.MEList = MEList
raw.datExpr = p.Wdata@assays$RNA@data[colnames(datExpr),]
raw.datExpr = t(as.matrix(raw.datExpr))
raw.MEList = WGCNA::moduleEigengenes(raw.datExpr, colors = dynamicColors)
p.MEList = raw.MEList
Modules of co-expression
For single cells We can see what are the expression levels of our co-expression modulesin the single cells. Using the dimensionality reduction provided. Default is a tSNE
xx=list()
yy=c(levels(as.factor(dynamicColors)))
for (i in 1:length(yy)) {
toplot = data.frame(Seurat::Embeddings(p.Wdata[[my.dr]]))
xx[[i]] = ggplot2::ggplot(toplot[order(p.MEList$averageExpr[,i]),],
ggplot2::aes_string(x=colnames(toplot)[1], y=colnames(toplot)[2])) +
ggplot2::geom_point(ggplot2::aes_string(color=p.MEList$averageExpr[order(p.MEList$averageExpr[,i]),i]), size=2) +
ggplot2::scale_size(range = c(1, 1)) +
ggplot2::theme_void() +
ggplot2::theme(legend.position="none") +
ggplot2::scale_colour_gradientn(colours = c("gray90", "gray90", yy[i], yy[i])) +
ggplot2::labs(colour=levels(as.factor(dynamicColors))[i])
}
gridExtra::grid.arrange(grobs=xx, ncol=4)
Create a list object with all the essential data from the analyses
WGCNA_data = list()
WGCNA_data[["datExpr"]] = datExpr
WGCNA_data[["dynamicMods"]] = dynamicMods
WGCNA_data[["MEList"]] = MEList
WGCNA_data[["MEs"]] = MEs
WGCNA_data[["modGenes"]] = xy
WGCNA_data[["MEList.sc"]] = p.MEList
WGCNA_data[["genesets"]]= genesets
WGCNA_data[["TOM"]]= TOM
WGCNA_data[["adjacency"]]= my.adjacency
saveRDS(WGCNA_data, file = paste0(params$dir,project_name,"_WGCNA_data_",my.date,".rds"))
To create the visualizations of the actual co-expression networks, we use this code. It also creates files that can be read into cytoscape
# we create a new directory where the network files will be
my.dirname = paste0(params$dir,project_name,"WGCNA_networks_",my.date,"/")
dir.create(my.dirname)
## Warning in dir.create(my.dirname): '/scicore/home/tschoppp/feregrin/test/
## scWGCNA_testing/E15test3WGCNA_networks_08.04.21' already exists
modules=unique(dynamicColors) # The modules
# To generate the files necessary for the network visualization in cytoscape
for (i in 1:length(modules)) {
mod=is.finite(match(dynamicColors, modules[i])) # Each module
cyt = WGCNA::exportNetworkToCytoscape(TOM[mod,mod],
edgeFile = paste( paste0(my.dirname,"CytoscapeInput-edges-"),
paste(modules[i], collapse="-"), ".txt", sep=""), # The name of the edge file
nodeFile = paste( paste0(my.dirname,"CytoscapeInput-nodes-"),
paste(modules[i], collapse="-"), ".txt", sep=""), # The node file
weighted = TRUE, #Puts the weights
threshold = 0.0, #Threshold for adjacency, 0=all genes
nodeNames = colnames(datExpr)[mod], #The names of the nodes/genes
altNodeNames = gnames[colnames(datExpr)[mod],2], #Other names
nodeAttr = geneModuleMembership[colnames(datExpr)[mod],i]) #Some more info about the nodes
}
# We build a list where we keep the network plots
netplots=list()
lcols = c(rep("black", length(unique(dynamicColors))))
lcols[(apply(col2rgb(levels(as.factor(dynamicColors))), 2,
function(x) (x[1]*0.299 + x[2]*0.587 + x[3]*0.114)) < 75)] = "white"
mynets = list()
# A loop that makes the network plots in R
for (i in 1:length(xx)) {
# Load directly edges tables from the file we created
mynetwork = read.table(paste0(my.dirname,"CytoscapeInput-edges-",levels(as.factor(dynamicColors))[i],".txt"),
header = T, stringsAsFactors = F, fill=T)
# We get rid of all the edges that have a very small weight. The cutoff set to keep at least ONE edge on the nodes
x = max( c( min(aggregate(mynetwork$weight, by = list(mynetwork$fromNode), max)$x),
min(aggregate(mynetwork$weight, by = list(mynetwork$toNode), max)$x) ) )
mynetwork=mynetwork[-which(mynetwork$weight < x),]
# We rescale the weights so that we have them from 0 to 1
mynetwork$weight01 = GGally::rescale01(mynetwork[,3])
# And then multiply them by 2, to give the heavy edges a 2 thickness, we ad 0.2 to not have completelly invisible edges
mynetwork[,3] = (GGally::rescale01(mynetwork[,3]) * 2) + 0.2
# Convert this edgelist into a network object
mynet = network::network(mynetwork, matrix.type="edgelist", ignore.eval=F)
# Load in the nodes table
mynodes = read.table(paste0(my.dirname,"CytoscapeInput-nodes-",levels(as.factor(dynamicColors))[i],".txt"),
header = T, stringsAsFactors = F, fill = T)
rownames(mynodes) <- mynodes$nodeName
# put in the membership value, from the ModuleMembership table, and rescale that from 0 to 1.
mynodes$membership = GGally::rescale01(geneModuleMembership[rownames(mynodes),i])
# We multiply by 30 to give a good range of sizes, plus 1 to avoid innexistant nodes
mynodes$membership= (mynodes$membership*30)+1
mynet = network::set.vertex.attribute(mynet, "membership", mynodes[network::network.vertex.names(mynet),"membership"])
mynets[[i]] = mynet
}
To check the results module by module, we report some GO terms and individual plots, with module sizes and gene names. First the GO terms analyses
if (params$GO==T) {
#Get the ENSEMBL to ENTREZ list from the BioMart
require(paste0("org.",my.sp,".eg.db"),character.only = T)
my.ENS2EG = get(paste0("org.",my.sp,".egENSEMBL2EG"), pos = paste0("package:org.",my.sp,".eg.db"))
IDs=as.list(my.ENS2EG)
#Create two lists, one for the DEGs and one for the GOs
dewg = list()
gowg = list()
# A loop that goes through the DEG files we created earlier
for(i in 1:length(yy)){ #as many clusters as we have
dewg[[i]] = xy[[i]] #Read the table
ex = sapply(dewg[[i]], function(x) AnnotationDbi::exists(x, my.ENS2EG)) #Which genes have an ENTREZ?
dewg[[i]] = dewg[[i]][ex] #Only those genes
dewg[[i]] = unlist(IDs[dewg[[i]]]) #The ENTREZ IDs
gowg[[i]] = limma::goana(dewg[[i]], species = my.sp, universe = unlist(IDs[my.gouni])) #The GO analysis
}
goplots=list()
goterms=list()
for (i in 1:length(yy)) {
toplot=limma::topGO(gowg[[i]], n= 50, ontology = c("BP"))
#Change to character and then back to factor, to keep the order from TopGO
toplot$Term = as.character(toplot$Term)
toplot$Term = factor(toplot$Term, levels = unique(toplot$Term))
goterms[[i]]=toplot
}
#TO make the acutal plot we're having, we combine all the GOs
g.plot = data.frame()
topterms = data.frame()
for (i in 1:length(goterms)) {
topterms = goterms[[i]][1:5,]
topterms$cluster = i
g.plot = rbind(g.plot, topterms)
}
g.plot$cluster = as.factor(g.plot$cluster)
g.plot$Term = droplevels(g.plot$Term)
my.terms = nchar(levels(g.plot$Term))>45
my.gterms = strtrim(levels(g.plot$Term), 45)
my.gterms[my.terms] = paste0(my.gterms[my.terms],"...")
levels(g.plot$Term) = my.gterms
my.terms = nchar(as.character(g.plot$Term))>45
my.gterms = strtrim(g.plot$Term, 45)
my.gterms[my.terms] = paste0(my.gterms[my.terms],"...")
ggplot2::ggplot(g.plot, ggplot2::aes(x=Term, y=-log10(P.DE), fill=cluster)) +
ggplot2::geom_point(ggplot2::aes(shape = cluster), size = 3) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) +
ggplot2::labs(x="GO Term", y="-log10 p. value") +
ggplot2::scale_fill_manual(name = "Module",
labels = yy,
values = yy) +
ggplot2::scale_shape_manual(name = "Module",
labels = yy,
values = rep(c(21,22,23,24,25),20)) +
ggplot2::coord_flip()
}
And here the report of each of the modules
# Go trhough the modules
for(i in 1:length(xx)){
# A table is done, where we put the genes names that are making-up the module, in rows of 10. We fill the las row with empty spaces
ktable1 = data.frame( matrix( c(gnames[xy[[i]],2][order(geneModuleMembership[xy[[i]],i], decreasing = T)],
rep(" ", 10-length(gnames[xy[[i]],2])%%10 )), ncol = 10, byrow = T ) )
colnames(ktable1) = rep(".", 10)
if (params$GO == T) {
# Another table, where we put the top 10 GO terms, in two columns
ktable2 = data.frame( goterms[[i]][1:10,])
ktable2[,5] = as.character(signif(ktable2[,5],4))
colnames(ktable2) = c("Term", "Ont", "N", "n", "Adj. p-value")
}
# We make a plot in which we show the mean expression level of the co-expression module
toplot = data.frame(Seurat::Embeddings(p.Wdata[[my.dr]]))
myplot = ggplot2::ggplot(toplot[order(p.MEList$averageExpr[,i]),],
ggplot2::aes_string(x=colnames(toplot)[1], y=colnames(toplot)[2])) +
ggplot2::geom_point(ggplot2::aes_string(color=p.MEList$averageExpr[order(p.MEList$averageExpr[,i]),i])) +
ggplot2::scale_size(range = c(1, 1)) +
ggplot2::theme_void() +
ggplot2::scale_colour_gradientn(colours = c("gray90", "gray90", yy[i], yy[i])) +
ggplot2::labs(colour=levels(as.factor(dynamicColors))[i])
mynet=mynets[[i]]
mynetplot = GGally::ggnet2(mynet,
mode = "fruchtermanreingold",
layout.par = list(repulse.rad=network::network.size(mynet)^1.1,
area=network::network.size(mynet)^2.3), # Give space in the middle
node.size = network::get.vertex.attribute(mynet,'membership'), max_size = 20, #The size of the nodes
node.color = levels(as.factor(dynamicColors))[i], # The color of the module
edge.size = "weight",
edge.color = "black",
edge.alpha = network::get.edge.attribute(mynet,'weight01'),
) +
ggplot2::theme(legend.position="none") +
ggplot2::geom_label(ggplot2::aes(label=gnames[network::network.vertex.names(mynet),2]),
fill = levels(as.factor(dynamicColors))[i],
alpha = 0.5,
color=lcols[i],
fontface = "bold")
# We put our mean expression plot toghether with the network plot
gridExtra::grid.arrange(grobs=list(myplot,mynetplot), nrow=2)
# # plot.new()
# # dev.off()
cat("\n")
# A table showing the number, color and size of the module
print(knitr::kable(data.frame(module=i, color=names(table(dynamicColors))[i], size=unname(table(dynamicColors)[i]))))
# cat("\n")
# The other two tables we just made
print(knitr::kable(ktable1))
if (params$GO == T) {
print(knitr::kable(ktable2))
}
cat("\n")
}
| Krt5 |
Perp |
Krt15 |
Sfn |
Krt14 |
Trim29 |
Fgfbp1 |
Lgals7 |
Wnt6 |
Trp63 |
| Mt2 |
Pkp3 |
Bcam |
Epcam |
Dsp |
Wnt7b |
Gjb6 |
Esrp1 |
Aqp3 |
Gjb2 |
| Kremen2 |
Col17a1 |
Dmkn |
Pdlim1 |
Rab38 |
Lmo1 |
Fermt1 |
Pkp1 |
Serpinb10 |
Wnt3 |
| Wnt10a |
Gata3 |
Meis2 |
Mapk13 |
Tfap2a |
Ckmt1 |
Tfap2b |
Cdh1 |
Ptprf |
Lypd6b |
| Serpinb5 |
Irf6 |
Cstdc5 |
Anxa8 |
Tfap2c |
Krt17 |
Barx2 |
Krtdap |
Fzd10 |
Irx4 |
| Igsf9 |
Hspb8 |
Lamc2 |
Irx2 |
Zfp296 |
Fras1 |
Itga3 |
Klc3 |
Ddr1 |
Gm13219 |
| Cebpa |
Esrp2 |
Frem2 |
Ppp1r14c |
Fzd6 |
Endou |
Chchd10 |
Lamb3 |
Prxl2a |
Hotairm1 |
| Wnt4 |
Car12 |
Ifi202b |
Pltp |
Them5 |
St14 |
Hoxb4 |
Etl4 |
Cgn |
Cldn6 |
| Egfr |
Jag1 |
Adh1 |
Plet1 |
Ces2g |
Mgst1 |
Nrarp |
Urah |
Ch25h |
Trim2 |
| Dpp4 |
Bicdl2 |
Akap17b |
Atp8b1 |
Pard6b |
Prss27 |
Rdh12 |
Tubb3 |
Nipal1 |
Spns2 |
| Gchfr |
Anxa9 |
Tmem184a |
Gprc5a |
Ly6g6d |
|
|
|
|
|
| GO:0060429 |
epithelium development |
BP |
910 |
32 |
3.103e-14 |
| GO:0048729 |
tissue morphogenesis |
BP |
570 |
26 |
3.575e-14 |
| GO:0002009 |
morphogenesis of an epithelium |
BP |
472 |
24 |
3.857e-14 |
| GO:0009888 |
tissue development |
BP |
1556 |
38 |
5.893e-12 |
| GO:0043588 |
skin development |
BP |
209 |
15 |
3.082e-11 |
| GO:0008544 |
epidermis development |
BP |
229 |
15 |
1.128e-10 |
| GO:0009887 |
animal organ morphogenesis |
BP |
864 |
26 |
4.227e-10 |
| GO:0048513 |
animal organ development |
BP |
2634 |
44 |
1.823e-08 |
| GO:0009913 |
epidermal cell differentiation |
BP |
132 |
10 |
4.322e-08 |
| GO:0007155 |
cell adhesion |
BP |
1010 |
25 |
5.033e-08 |
## Warning: Removed 3 rows containing missing values (geom_label).
| Crabp1 |
Egfl6 |
Tmem132c |
Prrx2 |
Vcam1 |
Mmp11 |
Twist1 |
Map1b |
Efna5 |
Tril |
| Nkd2 |
Prrx1 |
C1qtnf2 |
Irx3 |
Dpysl3 |
Col7a1 |
Ngfr |
a |
Adamts2 |
Zeb2 |
| Col6a3 |
Fez1 |
Lmna |
Rspo1 |
Ctsk |
Septin11 |
Serpinc1 |
Irx1 |
Cntfr |
Ednra |
| Sp5 |
Tmem119 |
Apela |
Col23a1 |
Frem1 |
Lix1 |
Col5a1 |
Ccn4 |
Col14a1 |
Ptk7 |
| Grem2 |
Eva1b |
Cpxm2 |
Adam33 |
Tbx18 |
Rasl11b |
Plxna4 |
Emx2 |
Sema5a |
Creb3l1 |
| Fam174b |
Calb1 |
Nt5e |
Cbr3 |
Ltbp1 |
Cd248 |
Hmgn3 |
Cpne5 |
Pdgfrb |
Kcnk2 |
| Cdo1 |
Cxcl14 |
Zfp503 |
Ppp1r14a |
Vegfd |
Itih5 |
Tac1 |
Gpc4 |
Ephb2 |
Spats2l |
| Nradd |
Rgs7bp |
Dnm3 |
Thy1 |
Vstm4 |
Wif1 |
Gm14964 |
St8sia2 |
Fndc1 |
Lrig1 |
| C1qtnf7 |
En1 |
C130021I20Rik |
Alx3 |
Axin2 |
Stmn2 |
Itga8 |
Egflam |
Thsd7a |
Hic1 |
| Pax1 |
Olfml3 |
Zfp536 |
Lama2 |
Cdc42ep5 |
Fgf10 |
Igfbp2 |
Bend5 |
Rhoj |
Rnd3 |
| Slit3 |
Igsf10 |
Dkk1 |
Unc5d |
Wnt11 |
Cox20 |
Svbp |
Hunk |
Slc7a10 |
Mme |
| Lrrn3 |
Kcna4 |
Cnih2 |
Tpbg |
Lin7a |
Tspan11 |
Septin7 |
Sdc2 |
Pappa2 |
Dpt |
| Chst2 |
Serping1 |
Scube2 |
Tgfbr3 |
Kcns3 |
Ism1 |
Frmd6 |
Col8a1 |
Mex3b |
Mylip |
| Vstm2b |
Nnmt |
Tex15 |
Ptprd |
Cspg5 |
Iglon5 |
Rai14 |
Cttnbp2 |
Smarca2 |
Hand2 |
| Cacna2d3 |
Bmp3 |
Amot |
Palm |
Plpp3 |
Cacng4 |
Rgs17 |
Adam22 |
Cyth3 |
Plppr3 |
| Fnbp1l |
Dtx4 |
Bex4 |
Scara3 |
Hand2os1 |
Lmx1b |
Homer2 |
NA |
Cplx2 |
Inhba |
| Samd14 |
Rbp1 |
Lsamp |
Fjx1 |
Pdlim4 |
Fap |
Gsta4 |
Pcdh11x |
Cald1 |
Frmd4a |
| Aspn |
Robo1 |
Rdh10 |
Vcan |
Adamts5 |
Wbp1 |
Stra6 |
Stox2 |
Dchs1 |
NA |
| Enox1 |
Fgf18 |
Scx |
Bex1 |
Fbn1 |
Ptchd4 |
Grik5 |
Uchl1 |
Sema3a |
Cdh2 |
| Insyn1 |
Fat4 |
Shd |
H2bu2 |
Fblim1 |
Klhdc8b |
Tmem26 |
Tnmd |
Rarres2 |
Postn |
| Pdlim3 |
Ebf2 |
Rimklb |
Gstt1 |
Sbk1 |
Mkx |
Adgrg2 |
Tpm2 |
Epha3 |
Fn1 |
| Thbs2 |
Kirrel3 |
Chodl |
Gulp1 |
Pianp |
Pdpn |
Gm10371 |
Pnck |
NA |
Tia1 |
| Prr16 |
Ccdc136 |
Sbspon |
Tmeff2 |
Pdgfc |
Tmem30b |
|
|
|
|
| GO:0009653 |
anatomical structure morphogenesis |
BP |
2239 |
94 |
5.912e-22 |
| GO:0032501 |
multicellular organismal process |
BP |
5039 |
147 |
1.41e-21 |
| GO:0048731 |
system development |
BP |
3637 |
122 |
2.079e-21 |
| GO:0007275 |
multicellular organism development |
BP |
4034 |
127 |
4.338e-20 |
| GO:0048856 |
anatomical structure development |
BP |
4376 |
132 |
1.711e-19 |
| GO:0032502 |
developmental process |
BP |
4644 |
134 |
4.243e-18 |
| GO:0009887 |
animal organ morphogenesis |
BP |
864 |
51 |
3.86e-17 |
| GO:0072359 |
circulatory system development |
BP |
971 |
53 |
2.329e-16 |
| GO:0051239 |
regulation of multicellular organismal process |
BP |
2519 |
87 |
1.223e-14 |
| GO:0048869 |
cellular developmental process |
BP |
3314 |
102 |
3.195e-14 |
## Warning: Removed 1 rows containing missing values (geom_label).
| Cd52 |
Coro1a |
Psmb8 |
Bin2 |
Laptm5 |
Rac2 |
Hcls1 |
Cd53 |
Evi2a |
Myo1g |
| Apbb1ip |
Fermt3 |
Selplg |
Vav1 |
H2-D1 |
Ptprc |
Mndal |
Napsa |
Srgn |
Plac8 |
| Lcp1 |
Psmb9 |
Wfdc17 |
Fmnl1 |
Nrros |
Fxyd5 |
Itgb2 |
Ccl6 |
Gpsm3 |
Nckap1l |
| Vsir |
Cytip |
Fcgr2b |
Fgd2 |
Lat2 |
Arhgap9 |
Gmfg |
Sp140 |
Cyp4f18 |
Ncf4 |
| Ighm |
Cfp |
H2-K1 |
Cd68 |
Casp1 |
Ptpro |
Lcp2 |
AI662270 |
Sh2d1b2 |
Slc15a3 |
| C3ar1 |
Cx3cr1 |
Ifitm6 |
Ptpn18 |
Cd44 |
Cxcl2 |
Lrmp |
Tnfaip8l2 |
Otulinl |
Cd74 |
| Sp110 |
Clec4d |
Bcl2a1b |
Lpxn |
Gm2a |
Pira2 |
Ccl3 |
Rgs2 |
Fyb |
Dok2 |
| Mcemp1 |
Ifi203 |
Pkib |
Ppfia4 |
Ifi211 |
Ly9 |
Plbd1 |
Siglece |
C1qb |
Fes |
| Dock2 |
Emb |
Il1b |
Tm6sf1 |
C1qa |
Hfe |
Tpd52 |
Cd14 |
Lyn |
C5ar1 |
| Ltb4r1 |
Rgs10 |
Trf |
Arhgap15 |
Hck |
Tifab |
Tlr2 |
Rgs18 |
Capg |
Cxcl3 |
| Glipr1 |
Cd33 |
S100a4 |
Osgin1 |
Slc11a1 |
Tifa |
Sirpa |
Zc3hav1 |
C1qc |
Syngr2 |
| Cd79b |
Rtp4 |
Rgs1 |
Klhl6 |
Ccr5 |
Adcy7 |
Oasl2 |
Phf11b |
Irf8 |
Rab7b |
| Ramp1 |
Plaur |
Ptpn7 |
Cd300lf |
Gm14548 |
Il4ra |
Arhgap45 |
Skap2 |
Bcl2a1d |
Fcrls |
| Mcub |
NA |
H2-Aa |
Lgals9 |
Bank1 |
H2-DMa |
Clec5a |
Ccl4 |
Pirb |
Cysltr1 |
| Rassf4 |
Milr1 |
H2-Eb1 |
Slamf9 |
Atp2a3 |
Ccl2 |
Ptprj |
Clec10a |
Il13ra1 |
Nlrp3 |
| Pmaip1 |
Ehd4 |
Csf3r |
Ms4a7 |
Hacd4 |
Fgl2 |
Tmem37 |
Clec2i |
Tuba4a |
B4galnt1 |
| Tmem106a |
Hpgds |
Adssl1 |
Cd83 |
H2-Oa |
Il1r2 |
Wfdc21 |
Emilin2 |
Il18rap |
Gpr65 |
| Slc9a3r1 |
Bst2 |
Fam129a |
Bmp2k |
Gch1 |
Mir22hg |
Prtn3 |
Rab32 |
Ahr |
Stom |
| Xlr |
Hhex |
H2-Ab1 |
Apobec3 |
Selenop |
Pglyrp1 |
Adap2os |
Adgre5 |
Fam107b |
Ccrl2 |
| F13a1 |
Lpcat2 |
Gpr137b-ps |
Dusp5 |
Osm |
Prkcb |
Tnni2 |
Klrd1 |
P2ry12 |
5430427O19Rik |
| Gas7 |
Pak1 |
H2-T23 |
Bcl2a1a |
Blnk |
Hmox1 |
Mrc1 |
Ang |
Trpv2 |
Hp |
| Cstdc4 |
S100a8 |
Pf4 |
Smim3 |
Hsd11b1 |
Atp1a3 |
H2-DMb1 |
S100a9 |
Mgl2 |
Gbp7 |
| Aph1c |
Kcnn4 |
F10 |
|
|
|
|
|
|
|
| GO:0006955 |
immune response |
BP |
908 |
98 |
1.446e-58 |
| GO:0002376 |
immune system process |
BP |
1714 |
122 |
6.129e-55 |
| GO:0006952 |
defense response |
BP |
966 |
81 |
7.363e-39 |
| GO:0002682 |
regulation of immune system process |
BP |
997 |
75 |
1.739e-32 |
| GO:0002684 |
positive regulation of immune system process |
BP |
683 |
63 |
5.928e-32 |
| GO:0050776 |
regulation of immune response |
BP |
540 |
56 |
6.835e-31 |
| GO:0045087 |
innate immune response |
BP |
467 |
52 |
3.901e-30 |
| GO:0045321 |
leukocyte activation |
BP |
644 |
59 |
1.163e-29 |
| GO:0001775 |
cell activation |
BP |
729 |
62 |
2.041e-29 |
| GO:0050778 |
positive regulation of immune response |
BP |
429 |
47 |
7.127e-27 |
| Ms4a6c |
Ctss |
Pld4 |
Clec4a3 |
Mpeg1 |
Cybb |
Ly86 |
Clec4a2 |
Igsf6 |
Lyz2 |
| Clec4a1 |
Ms4a6b |
Adgre1 |
Ebi3 |
AB124611 |
Trem2 |
Alox5ap |
Apoc2 |
Slfn2 |
Clec12a |
| Cd300c2 |
Ms4a6d |
Clec4n |
Cd48 |
Lst1 |
Fcgr3 |
Ccr2 |
Ctsc |
Tnfrsf13b |
Ms4a4c |
| Plek |
Ifi27l2a |
Unc93b1 |
Csf1r |
Ifi204 |
Ncf2 |
Ccr1 |
Apobec1 |
Cyth4 |
Ccl9 |
| Tyrobp |
Lrrc25 |
Irf5 |
Spi1 |
Cd86 |
Gm49339 |
Fcgr1 |
Ptafr |
Fcer1g |
P2ry6 |
| Ifi207 |
Rnase6 |
Epsti1 |
Clec7a |
Pilra |
Ptpn6 |
Myo1f |
Aif1 |
|
|
| GO:0006952 |
defense response |
BP |
966 |
28 |
4.625e-18 |
| GO:0006955 |
immune response |
BP |
908 |
27 |
1.244e-17 |
| GO:0002376 |
immune system process |
BP |
1714 |
34 |
2.37e-17 |
| GO:0009605 |
response to external stimulus |
BP |
1557 |
28 |
9.315e-13 |
| GO:0045087 |
innate immune response |
BP |
467 |
17 |
2.309e-12 |
| GO:0051707 |
response to other organism |
BP |
590 |
18 |
8.772e-12 |
| GO:0043207 |
response to external biotic stimulus |
BP |
592 |
18 |
9.28e-12 |
| GO:0009607 |
response to biotic stimulus |
BP |
617 |
18 |
1.844e-11 |
| GO:0002274 |
myeloid leukocyte activation |
BP |
158 |
11 |
3.032e-11 |
| GO:0006954 |
inflammatory response |
BP |
494 |
16 |
6.654e-11 |
| Top2a |
Birc5 |
Cdca8 |
Nusap1 |
Ccna2 |
Hmgb2 |
Cdca3 |
Ube2c |
Tpx2 |
Smc4 |
| Tacc3 |
Spc25 |
Aurkb |
Spc24 |
Racgap1 |
Prc1 |
Cks2 |
Pbk |
Lockd |
Tuba1b |
| Smc2 |
H2az1 |
Kif23 |
Mki67 |
Hmmr |
Pclaf |
Ccnb1 |
Incenp |
Ube2s |
Kif22 |
| Cenpe |
Stmn1 |
Cdk1 |
Plk1 |
Cenpf |
Nuf2 |
Cdc20 |
Kifc1 |
Tubb5 |
Tubb4b |
| H2ax |
Mis18bp1 |
H2az2 |
Melk |
Ccnb2 |
Bub3 |
Ndc80 |
Cenpa |
Bub1b |
Knstrn |
| Ncapd2 |
Depdc1a |
Pimreg |
Lmnb1 |
Tk1 |
Cdkn2c |
Arl6ip1 |
Rrm2 |
Aurka |
Kif20a |
| Ckap2l |
Asf1b |
H1f10 |
Shcbp1 |
Bub1 |
Sapcd2 |
Ccdc34 |
Nucks1 |
Kif20b |
Psrc1 |
| Hmgn2 |
Mxd3 |
Sgo2a |
Neil3 |
Dlgap5 |
Anln |
Tyms |
Knl1 |
Ncapg |
Tmpo |
| Ckap2 |
Nde1 |
Kif2c |
Kpna2 |
Cdkn3 |
Hist1h2ap |
Cit |
Cdc25c |
Dek |
Esco2 |
| Calm2 |
Jpt1 |
Ube2t |
Ptma |
Tubb6 |
Gmnn |
Aspm |
Rad51 |
H2ac8 |
H4c9 |
| Tuba1c |
Dut |
Hmgb3 |
Plk4 |
Pkmyt1 |
Gas2l3 |
Atad2 |
H1f0 |
Lig1 |
Cbx3 |
| Tcf19 |
H1f4 |
Uhrf1 |
Cep89 |
Syce2 |
Lsm6 |
Gm47283 |
Fxyd6 |
Rpgrip1 |
Mcm5 |
| Supt16 |
Slbp |
1500009L16Rik |
Gins2 |
Slc24a5 |
Pcna |
Chaf1b |
Wdhd1 |
Xist |
Gli1 |
| Ptms |
Slc29a1 |
Hells |
Hs6st2 |
Mid1 |
Pik3r3 |
Syne2 |
Sms |
Hoxc10 |
Clasp2 |
| Nsg1 |
Spock3 |
Mcm3 |
Ccne1 |
Lnpk |
Hoxa9 |
Fhl1 |
Fzd4 |
Mcm2 |
Tafa4 |
| Col4a2 |
|
|
|
|
|
|
|
|
|
| GO:0007049 |
cell cycle |
BP |
1408 |
92 |
2.361e-54 |
| GO:0000278 |
mitotic cell cycle |
BP |
730 |
67 |
1.762e-46 |
| GO:0022402 |
cell cycle process |
BP |
972 |
74 |
3.145e-46 |
| GO:1903047 |
mitotic cell cycle process |
BP |
596 |
60 |
1.872e-43 |
| GO:0051301 |
cell division |
BP |
516 |
56 |
4.151e-42 |
| GO:0000280 |
nuclear division |
BP |
339 |
42 |
1.779e-33 |
| GO:0048285 |
organelle fission |
BP |
380 |
42 |
2.117e-31 |
| GO:0140014 |
mitotic nuclear division |
BP |
235 |
35 |
1.23e-30 |
| GO:0051276 |
chromosome organization |
BP |
956 |
58 |
1.056e-29 |
| GO:0007059 |
chromosome segregation |
BP |
275 |
36 |
1.817e-29 |
| Tspo |
Gpx1 |
Nfkbia |
Rap1b |
Msn |
Actr3 |
Cdk2ap2 |
Ier5 |
Lrrfip1 |
Prr29 |
| Serpina3g |
Lat |
Il22 |
Sec11c |
Tgfb1 |
Cd4 |
Tox2 |
Zap70 |
Ifngr1 |
Ikzf2 |
| Cited4 |
Nrgn |
Arg1 |
Il17re |
Cyfip2 |
Septin1 |
Cited2 |
Hpcal1 |
Tec |
Ifi30 |
| Actg1 |
Macroh2a1 |
Dusp1 |
Espn |
Arl4c |
Tspan13 |
Gsn |
Samhd1 |
Jmjd1c |
Trim8 |
| Cmtm7 |
Pik3r1 |
Cdkn1a |
Ifrd1 |
Zfp36 |
Gsto1 |
Samd10 |
Hexb |
Mzb1 |
Cnn2 |
| Fam124b |
Sdf2l1 |
Car2 |
Ctsd |
Zyx |
Ahnak |
Akr1c13 |
Rrad |
Anxa1 |
Dnase2a |
| Myb |
Kdm7a |
Ptgs2 |
Cxcl16 |
Ly6a |
Slc7a11 |
Prr11 |
Crip1 |
Asb17 |
Serp1 |
| GO:0002376 |
immune system process |
BP |
1714 |
32 |
5.504e-12 |
| GO:0002682 |
regulation of immune system process |
BP |
997 |
21 |
1.029e-08 |
| GO:0006955 |
immune response |
BP |
908 |
19 |
6.893e-08 |
| GO:0034097 |
response to cytokine |
BP |
637 |
16 |
7.865e-08 |
| GO:0070887 |
cellular response to chemical stimulus |
BP |
2093 |
29 |
9.315e-08 |
| GO:0006950 |
response to stress |
BP |
2695 |
33 |
1.464e-07 |
| GO:0006952 |
defense response |
BP |
966 |
19 |
1.823e-07 |
| GO:0071310 |
cellular response to organic substance |
BP |
1686 |
25 |
3.017e-07 |
| GO:0010033 |
response to organic substance |
BP |
2075 |
28 |
3.075e-07 |
| GO:0050896 |
response to stimulus |
BP |
5705 |
50 |
3.383e-07 |
| Mfap4 |
Fstl1 |
Mdk |
Gas1 |
Mfap2 |
Col3a1 |
Col1a1 |
Fbn2 |
Cdh11 |
Pdgfra |
| Tcf4 |
Il11ra1 |
Mmp14 |
6330403K07Rik |
Fbln5 |
Macroh2a2 |
Col1a2 |
Igfbp4 |
Sfrp2 |
Col6a2 |
| Gas2 |
Tnfaip6 |
Runx1t1 |
Clmp |
Wnt5a |
Cxcl12 |
Bgn |
Loxl1 |
Nfib |
Ubb |
| Fgfr1 |
Hoxd12 |
Sox11 |
Pfn2 |
Marcksl1 |
Zfp36l1 |
Tmsb10 |
Vim |
Gm9844 |
Tmsb4x |
| GO:0001501 |
skeletal system development |
BP |
465 |
14 |
8.579e-12 |
| GO:0009888 |
tissue development |
BP |
1556 |
20 |
4.213e-10 |
| GO:0009653 |
anatomical structure morphogenesis |
BP |
2239 |
23 |
7.685e-10 |
| GO:0060322 |
head development |
BP |
553 |
13 |
1.194e-09 |
| GO:0030334 |
regulation of cell migration |
BP |
768 |
14 |
6.247e-09 |
| GO:0030198 |
extracellular matrix organization |
BP |
229 |
9 |
8.173e-09 |
| GO:0035295 |
tube development |
BP |
948 |
15 |
1.03e-08 |
| GO:2000145 |
regulation of cell motility |
BP |
800 |
14 |
1.052e-08 |
| GO:0007417 |
central nervous system development |
BP |
692 |
13 |
1.778e-08 |
| GO:0040012 |
regulation of locomotion |
BP |
854 |
14 |
2.412e-08 |
| Cyp11a1 |
Il4 |
Cpa3 |
Il1rl1 |
Cd200r3 |
Cma1 |
Hdc |
Prss34 |
Alox5 |
Skap1 |
| Mcpt8 |
Cd200r4 |
Adora3 |
Tpsg1 |
Tpsb2 |
Hs3st1 |
Cst7 |
Gata2 |
Cela1 |
Smpx |
| Tph1 |
Slc45a3 |
Rab27b |
Tpsab1 |
Il13 |
Kit |
Gnaz |
Man2b1 |
Prkcq |
Gadd45a |
| Tesc |
Gyg |
B2m |
Slc7a5 |
Ftl1 |
Plin2 |
Cebpb |
Cyba |
Klk8 |
Ubash3b |
| Nr4a2 |
Ly6e |
Pim1 |
Snx2 |
Tiparp |
Metrnl |
Fth1 |
Psap |
Sat1 |
Gstt2 |
| GO:0006954 |
inflammatory response |
BP |
494 |
15 |
9.618e-11 |
| GO:0032101 |
regulation of response to external stimulus |
BP |
601 |
13 |
1.208e-07 |
| GO:0006952 |
defense response |
BP |
966 |
16 |
1.238e-07 |
| GO:0043303 |
mast cell degranulation |
BP |
37 |
5 |
1.875e-07 |
| GO:0002279 |
mast cell activation involved in immune response |
BP |
38 |
5 |
2.154e-07 |
| GO:0002448 |
mast cell mediated immunity |
BP |
38 |
5 |
2.154e-07 |
| GO:0033008 |
positive regulation of mast cell activation involved in immune response |
BP |
17 |
4 |
3.284e-07 |
| GO:0043306 |
positive regulation of mast cell degranulation |
BP |
17 |
4 |
3.284e-07 |
| GO:0150076 |
neuroinflammatory response |
BP |
47 |
5 |
6.425e-07 |
| GO:0033005 |
positive regulation of mast cell activation |
BP |
21 |
4 |
8.171e-07 |
| Bcl11b |
Tnfrsf19 |
Megf6 |
Ass1 |
Ank3 |
Fzd10os |
S100a11 |
Acaa2 |
Fst |
Gja1 |
| Tom1l1 |
Ccdc3 |
Sult5a1 |
Cdh3 |
Wnt16 |
Ltbp4 |
Anxa2 |
Efcab15 |
1110028F18Rik |
Susd4 |
| Svil |
Mpped1 |
Dlx4os |
Calm4 |
Ociad2 |
Sox15 |
Dlx2 |
Fam83f |
Cldn1 |
Limk2 |
| Sbsn |
|
|
|
|
|
|
|
|
|
| GO:0043616 |
keratinocyte proliferation |
BP |
38 |
4 |
1.26e-06 |
| GO:0043588 |
skin development |
BP |
209 |
6 |
4.888e-06 |
| GO:0008544 |
epidermis development |
BP |
229 |
5 |
0.0001212 |
| GO:0021877 |
forebrain neuron fate commitment |
BP |
9 |
2 |
0.0001618 |
| GO:0009888 |
tissue development |
BP |
1556 |
11 |
0.0002589 |
| GO:0010644 |
cell communication by electrical coupling |
BP |
12 |
2 |
0.0002954 |
| GO:0030855 |
epithelial cell differentiation |
BP |
448 |
6 |
0.0003435 |
| GO:0042475 |
odontogenesis of dentin-containing tooth |
BP |
67 |
3 |
0.0004008 |
| GO:0060429 |
epithelium development |
BP |
910 |
8 |
0.0005477 |
| GO:2000810 |
regulation of bicellular tight junction assembly |
BP |
17 |
2 |
0.0006046 |
| Mlana |
Tyr |
Dct |
Slc45a2 |
Pax3 |
Pmel |
Ptgds |
Gpnmb |
Foxd3 |
Mcoln3 |
| Ednrb |
Trpm1 |
Plp1 |
Syngr1 |
Insc |
Bace2 |
Sox10 |
Npy |
Gsta1 |
Nkain4 |
| Phyhipl |
Slc26a7 |
|
|
|
|
|
|
|
|
| GO:0043473 |
pigmentation |
BP |
91 |
7 |
7.477e-11 |
| GO:0048066 |
developmental pigmentation |
BP |
48 |
5 |
1.099e-08 |
| GO:0042438 |
melanin biosynthetic process |
BP |
19 |
4 |
1.93e-08 |
| GO:0006582 |
melanin metabolic process |
BP |
21 |
4 |
2.974e-08 |
| GO:0044550 |
secondary metabolite biosynthetic process |
BP |
24 |
4 |
5.263e-08 |
| GO:0046189 |
phenol-containing compound biosynthetic process |
BP |
37 |
4 |
3.226e-07 |
| GO:0019748 |
secondary metabolic process |
BP |
46 |
4 |
7.895e-07 |
| GO:0046148 |
pigment biosynthetic process |
BP |
47 |
4 |
8.62e-07 |
| GO:0042440 |
pigment metabolic process |
BP |
56 |
4 |
1.758e-06 |
| GO:0018958 |
phenol-containing compound metabolic process |
BP |
69 |
4 |
4.081e-06 |
## Warning: Removed 1 rows containing missing values (geom_label).
| S100a14 |
Cldn3 |
Sprr1a |
Gm16136 |
Ppl |
Mab21l4 |
Rab25 |
Nectin4 |
Tacstd2 |
Aldh3b2 |
| Bspry |
Cldn4 |
Lypd3 |
Upk1b |
Krt4 |
Krt19 |
Asprv1 |
Ovol1 |
Areg |
Paqr5 |
| Krt6a |
Cldn23 |
Mal |
Sult2b1 |
Dsc2 |
Clic3 |
Gpr87 |
Krt7 |
Fam25c |
Ly6d |
| Upk3bl |
Grhl3 |
Spint1 |
Ly6g6e |
Rhov |
Krt18 |
Adtrp |
Cers3 |
Hspb1 |
Grhl1 |
| Pdzk1ip1 |
Crb3 |
Sptlc3 |
Ephx3 |
Sp6 |
Dsg1a |
Phldb3 |
Map3k6 |
Evpl |
Tmem125 |
| Krt8 |
Cnfn |
Ablim1 |
Slc2a3 |
Capns2 |
Gng13 |
Nebl |
Smtnl2 |
Tmem40 |
Jup |
| Cst6 |
Mall |
Csta1 |
NA |
Scel |
Tent5b |
Il1rn |
Paqr6 |
Zfp750 |
Snx31 |
| Kctd1 |
Mpzl3 |
Eps8l2 |
Lor |
Sytl1 |
Psca |
Aldh1a7 |
Prss8 |
Arap2 |
Krt1 |
| Gipc2 |
Tmem79 |
Dsc3 |
Klf5 |
Prom2 |
|
|
|
|
|
| GO:0043588 |
skin development |
BP |
209 |
17 |
3.483e-15 |
| GO:0008544 |
epidermis development |
BP |
229 |
16 |
2.644e-13 |
| GO:0031424 |
keratinization |
BP |
15 |
7 |
1.148e-12 |
| GO:0009913 |
epidermal cell differentiation |
BP |
132 |
12 |
1.46e-11 |
| GO:0030216 |
keratinocyte differentiation |
BP |
82 |
10 |
4.228e-11 |
| GO:0030855 |
epithelial cell differentiation |
BP |
448 |
18 |
8.352e-11 |
| GO:0060429 |
epithelium development |
BP |
910 |
23 |
1.379e-09 |
| GO:0061436 |
establishment of skin barrier |
BP |
17 |
5 |
3.607e-08 |
| GO:0033561 |
regulation of water loss via skin |
BP |
22 |
5 |
1.5e-07 |
| GO:0009888 |
tissue development |
BP |
1556 |
26 |
4.686e-07 |
| mt-Cytb |
mt-Atp6 |
mt-Co3 |
mt-Co2 |
mt-Nd1 |
Fabp5 |
mt-Co1 |
mt-Nd2 |
Pard6g |
Sostdc1 |
| Tmem132a |
Npnt |
Spry1 |
1700093K21Rik |
mt-Nd5 |
Sema3f |
Cachd1 |
Cygb |
Ddit4 |
Hspa1a |
| Nr2e3 |
Cyb5a |
Lypd6 |
Irx6 |
Dcn |
Rhbdl3 |
Hoxc8 |
Ier3 |
Astn2 |
Bmp7 |
| Msx2 |
Edar |
Jag2 |
Mfhas1 |
Emp2 |
Syt1 |
Hoxc13 |
Ptprz1 |
Gdpd1 |
Foxo6 |
| Prr5l |
Nav2 |
Notch3 |
Dkk4 |
Unc5b |
Tnfrsf12a |
F2rl1 |
Syt13 |
Narf |
Cldn7 |
| Arhgdib |
Kctd11 |
5730522E02Rik |
Zfp185 |
|
|
|
|
|
|
| GO:0046034 |
ATP metabolic process |
BP |
211 |
9 |
6.985e-08 |
| GO:0042773 |
ATP synthesis coupled electron transport |
BP |
60 |
6 |
8.257e-08 |
| GO:0009205 |
purine ribonucleoside triphosphate metabolic process |
BP |
234 |
9 |
1.691e-07 |
| GO:0009126 |
purine nucleoside monophosphate metabolic process |
BP |
236 |
9 |
1.818e-07 |
| GO:0009167 |
purine ribonucleoside monophosphate metabolic process |
BP |
236 |
9 |
1.818e-07 |
| GO:0009199 |
ribonucleoside triphosphate metabolic process |
BP |
238 |
9 |
1.953e-07 |
| GO:0009161 |
ribonucleoside monophosphate metabolic process |
BP |
240 |
9 |
2.097e-07 |
| GO:0009144 |
purine nucleoside triphosphate metabolic process |
BP |
242 |
9 |
2.25e-07 |
| GO:0009123 |
nucleoside monophosphate metabolic process |
BP |
247 |
9 |
2.675e-07 |
| GO:0009141 |
nucleoside triphosphate metabolic process |
BP |
257 |
9 |
3.74e-07 |
| Cd37 |
Trbc2 |
Hcst |
Ptprcap |
Itgal |
Cd69 |
Ltb |
Il7r |
Il18r1 |
Cd7 |
| Klrb1b |
Il2rg |
Sla |
Nkg7 |
Itgb7 |
Ccl5 |
Gpr183 |
Cd160 |
Ikzf1 |
Parvg |
| Itk |
Rasal3 |
Trbc1 |
Cd3g |
Tnfaip3 |
Tnfrsf25 |
Tagap |
Tnfrsf9 |
Cxcr6 |
Tcf7 |
| Samsn1 |
Fam83a |
Gpr171 |
Rora |
Xlr4b |
Xlr4c |
Slc6a13 |
Xlr4a |
Ctsw |
Lta |
| Rab37 |
Tmem154 |
Icos |
Cd28 |
Asb2 |
Serpinb1a |
Rhoh |
Pfn1 |
Sh3bgrl3 |
Crem |
| Npl |
Icam1 |
Cd47 |
Tagln2 |
Ckb |
Ccr7 |
Flt3 |
Klf6 |
Esyt1 |
Itm2b |
| Ecm1 |
Ifitm1 |
Rassf2 |
Arpc1b |
Irak2 |
Abca3 |
Serinc3 |
Slc16a10 |
Clic1 |
Actb |
| Junb |
Cxcl10 |
Csrnp1 |
Arl6ip5 |
Dleu2 |
Rbm38 |
Slc25a5 |
S100a13 |
Rnf130 |
Akr1b3 |
| Rnasel |
Fam111a |
Zfp263 |
Ctnnd2 |
Apoc1 |
Runx1 |
Entpd1 |
Pou2f2 |
Mitf |
|
| GO:0002376 |
immune system process |
BP |
1714 |
44 |
1.186e-18 |
| GO:0042110 |
T cell activation |
BP |
373 |
21 |
4.866e-15 |
| GO:0002682 |
regulation of immune system process |
BP |
997 |
31 |
7.02e-15 |
| GO:0046649 |
lymphocyte activation |
BP |
538 |
24 |
7.118e-15 |
| GO:0001775 |
cell activation |
BP |
729 |
27 |
9.683e-15 |
| GO:0006955 |
immune response |
BP |
908 |
29 |
3.485e-14 |
| GO:0045321 |
leukocyte activation |
BP |
644 |
25 |
4.162e-14 |
| GO:0002521 |
leukocyte differentiation |
BP |
432 |
20 |
9.331e-13 |
| GO:0002252 |
immune effector process |
BP |
526 |
21 |
3.933e-12 |
| GO:0046631 |
alpha-beta T cell activation |
BP |
122 |
12 |
8.744e-12 |
| Hoxd13 |
Ccnd2 |
Sdc1 |
Foxp2 |
Ntng1 |
Ccnd1 |
Creb5 |
Hoxa11os |
Gdf5 |
Serinc2 |
| Adgrl3 |
Hoxd11 |
Dkk3 |
Fbln2 |
Mpped2 |
Col12a1 |
Pgrmc1 |
Aopep |
Epha4 |
Pthlh |
| Hhip |
Msx1 |
Car14 |
H3f3b |
Foxp4 |
Tob1 |
Prdm16 |
Sema3c |
Hmcn1 |
Lhx2 |
| Tgfb3 |
Ptn |
Igsf3 |
Serpine2 |
Col8a2 |
Serpinf1 |
Erg |
Mn1 |
Lmo4 |
Hes1 |
| Tgfb2 |
Fxyd7 |
Ssbp2 |
Syt11 |
Thsd4 |
Mecomos |
Pbx1 |
Hoxa10 |
Nrxn1 |
Nrtn |
| Frk |
Kcnd2 |
Axl |
Bmp1 |
Fam171b |
Erc2 |
Nell2 |
Plxdc2 |
Tenm3 |
Car11 |
| Kcnmb4 |
Dlg5 |
Srsf12 |
Sv2a |
Tmtc2 |
2700069I18Rik |
Ank2 |
Mllt3 |
Htra1 |
Nrn1 |
| Slit2 |
Pcdh10 |
|
|
|
|
|
|
|
|
| GO:0048513 |
animal organ development |
BP |
2634 |
39 |
2.857e-12 |
| GO:0009888 |
tissue development |
BP |
1556 |
28 |
2.396e-10 |
| GO:0048732 |
gland development |
BP |
356 |
14 |
1.466e-09 |
| GO:0035295 |
tube development |
BP |
948 |
21 |
2.388e-09 |
| GO:0032502 |
developmental process |
BP |
4644 |
47 |
2.662e-09 |
| GO:0009887 |
animal organ morphogenesis |
BP |
864 |
20 |
3.003e-09 |
| GO:0032501 |
multicellular organismal process |
BP |
5039 |
49 |
3.003e-09 |
| GO:0030154 |
cell differentiation |
BP |
3150 |
38 |
3.688e-09 |
| GO:2000026 |
regulation of multicellular organismal development |
BP |
1758 |
28 |
3.964e-09 |
| GO:0050793 |
regulation of developmental process |
BP |
2188 |
31 |
6.499e-09 |
| Actc1 |
Tnnt2 |
Tnnt1 |
Cdh15 |
Mymx |
Myog |
Chrna1 |
Mymk |
Vgll2 |
Des |
| Chrng |
Rbm24 |
Neb |
Atp2a1 |
Ank1 |
Arpp21 |
Ttn |
Myod1 |
Fitm1 |
Cryab |
| Tnni1 |
Myl1 |
Pnmal2 |
Gatm |
Jsrp1 |
Rtn2 |
Megf10 |
Mylpf |
Acta2 |
Klhl41 |
| Hspb2 |
Tnik |
Cap2 |
Nes |
Iffo1 |
Tex14 |
Fndc5 |
Mrln |
Myl4 |
Traf3ip3 |
| Il17b |
Pitx2 |
Pgam2 |
Srpk3 |
Mstn |
Srl |
Rgs16 |
Olfml2b |
Hdac11 |
Myh3 |
| Sln |
Flnc |
Car3 |
Tnnc2 |
Cck |
Tnnc1 |
Celf2 |
Myl6b |
Tubb2b |
Septin4 |
| Ralgps2 |
Igfbp3 |
Prkaca |
Pdlim7 |
Pgm5 |
Mycl |
Acta1 |
Casq2 |
Dll1 |
Ssc5d |
| Parm1 |
Tppp3 |
Cox6a2 |
Unc45b |
Casz1 |
Ccdc155 |
Tmem35a |
Nexn |
Fmo1 |
Arhgdig |
| Lrrn1 |
Kcne1l |
Tceal6 |
Eya2 |
Cited1 |
AW551984 |
Gpr37 |
Dbndd1 |
Six1 |
Frmd4b |
| Tln2 |
Msc |
Vash2 |
Pdgfa |
Tmem200a |
Rxrg |
Shisal2a |
Hspd1 |
Ptgis |
Fam110b |
| Spon1 |
Itga7 |
Trim63 |
Myf5 |
Shisa2 |
Rai2 |
Lurap1l |
Epor |
Tcea3 |
Mad2l2 |
| Sypl2 |
Necab1 |
Bcl7a |
Rhobtb1 |
Dbx1 |
Rplp1 |
Dcx |
Cbln2 |
Camk2n1 |
|
| GO:0061061 |
muscle structure development |
BP |
585 |
38 |
4.37e-24 |
| GO:0007517 |
muscle organ development |
BP |
350 |
28 |
4.561e-20 |
| GO:0003012 |
muscle system process |
BP |
312 |
26 |
4.263e-19 |
| GO:0014706 |
striated muscle tissue development |
BP |
373 |
27 |
3.078e-18 |
| GO:0060537 |
muscle tissue development |
BP |
394 |
27 |
1.246e-17 |
| GO:0006936 |
muscle contraction |
BP |
217 |
20 |
1.113e-15 |
| GO:0060538 |
skeletal muscle organ development |
BP |
170 |
18 |
2.901e-15 |
| GO:0006941 |
striated muscle contraction |
BP |
123 |
16 |
4.153e-15 |
| GO:0007519 |
skeletal muscle tissue development |
BP |
163 |
17 |
2.335e-14 |
| GO:0051146 |
striated muscle cell differentiation |
BP |
264 |
20 |
4.833e-14 |
| Crabp2 |
Lgals1 |
Ddah2 |
Marcks |
Lpar1 |
Tle5 |
Nbl1 |
Col26a1 |
Col5a2 |
Basp1 |
| Rbms3 |
Lhfp |
Epb41l3 |
Nnat |
Fbln1 |
Tead2 |
Bcl11a |
Dbn1 |
Podxl2 |
Tpm1 |
| Col6a1 |
S1pr3 |
Chd3 |
Tuba1a |
Phldb2 |
Ctxn1 |
Ntf3 |
Cacnb3 |
Glipr2 |
Dclk1 |
| Pcdh18 |
Myh10 |
Ogn |
Dkk2 |
Nfia |
Lhfpl2 |
Ly6h |
Foxp1 |
Csrp2 |
Lox |
| Myo1b |
Zfhx4 |
Sncaip |
Angptl4 |
Sulf1 |
Sox4 |
Cped1 |
Adamtsl1 |
Efnb3 |
Osr2 |
| Robo2 |
Osr1 |
Mir100hg |
Bmp4 |
Cacna1g |
Aplp1 |
Nfatc4 |
Csrp1 |
Agtr2 |
Crlf1 |
| Cmtm3 |
Epha7 |
Mecom |
Igfbp5 |
Col4a1 |
|
|
|
|
|
| GO:0048856 |
anatomical structure development |
BP |
4376 |
46 |
2.725e-11 |
| GO:0007275 |
multicellular organism development |
BP |
4034 |
44 |
3.949e-11 |
| GO:0009653 |
anatomical structure morphogenesis |
BP |
2239 |
33 |
4.221e-11 |
| GO:0072001 |
renal system development |
BP |
251 |
13 |
7.913e-11 |
| GO:0009888 |
tissue development |
BP |
1556 |
27 |
1.798e-10 |
| GO:0032502 |
developmental process |
BP |
4644 |
46 |
2.579e-10 |
| GO:0001655 |
urogenital system development |
BP |
289 |
13 |
4.53e-10 |
| GO:0032501 |
multicellular organismal process |
BP |
5039 |
47 |
1.119e-09 |
| GO:0048731 |
system development |
BP |
3637 |
39 |
3.535e-09 |
| GO:0007399 |
nervous system development |
BP |
1857 |
26 |
4.442e-08 |
## Warning: Removed 1 rows containing missing values (geom_label).
| Ddit4l |
Mmp13 |
Gpx3 |
Rbp4 |
Cp |
Ffar4 |
Ibsp |
Steap4 |
Fabp3 |
Slc36a2 |
| Enpp6 |
Ocm |
AW112010 |
Itgb3 |
Fam20c |
Cemip |
Maf |
Agt |
Hpgd |
Bmp8a |
| 2200002D01Rik |
Dapk2 |
Phex |
Mgst2 |
Serpini1 |
Hand1 |
Tcim |
Lpl |
Frzb |
Mmp9 |
| Loxl4 |
Chst1 |
Lratd1 |
Plaat3 |
Sp7 |
Irx5 |
Gm10638 |
Ptges |
Clu |
Cdkn2b |
| Csf1 |
Hmgcs2 |
Tnfaip2 |
Entpd3 |
Cds1 |
B3galt2 |
A230083N12Rik |
Podnl1 |
9130024F11Rik |
Dmp1 |
| Syna |
Dlx5 |
Cd55 |
Foxq1 |
Rcan2 |
Slpi |
Pla2g4a |
Lgr6 |
Gm19705 |
Rasl11a |
| Jam2 |
NA |
Ackr3 |
Nos1ap |
Adamts9 |
Ank |
|
|
|
|
| GO:0001503 |
ossification |
BP |
330 |
11 |
2.063e-07 |
| GO:0009888 |
tissue development |
BP |
1556 |
21 |
2.042e-06 |
| GO:0043062 |
extracellular structure organization |
BP |
263 |
9 |
2.366e-06 |
| GO:1900020 |
positive regulation of protein kinase C activity |
BP |
7 |
3 |
2.944e-06 |
| GO:1900019 |
regulation of protein kinase C activity |
BP |
7 |
3 |
2.944e-06 |
| GO:0030198 |
extracellular matrix organization |
BP |
229 |
8 |
7.758e-06 |
| GO:0061448 |
connective tissue development |
BP |
251 |
8 |
1.513e-05 |
| GO:0031214 |
biomineral tissue development |
BP |
126 |
6 |
2.035e-05 |
| GO:0048513 |
animal organ development |
BP |
2634 |
26 |
2.805e-05 |
| GO:0010743 |
regulation of macrophage derived foam cell differentiation |
BP |
16 |
3 |
4.576e-05 |
| Col9a3 |
Col9a1 |
Col2a1 |
Col11a1 |
Col9a2 |
Mia |
Hapln1 |
Cnmd |
Matn1 |
Acan |
| Col27a1 |
Col11a2 |
Matn3 |
Fkbp9 |
Susd5 |
Cthrc1 |
Plod2 |
Rcn3 |
Snorc |
Matn4 |
| Papss2 |
Chadl |
P4ha1 |
Comp |
Serpinh1 |
Fibin |
Tspan4 |
Sox9 |
Wwp2 |
Scrg1 |
| Dlk1 |
Itm2a |
Ugdh |
S100b |
Gale |
Kdelr3 |
Loxl3 |
Slc16a3 |
Fkbp10 |
Prelp |
| Islr |
Vkorc1 |
Fgfr3 |
Rflna |
Rcn2 |
Peg3 |
C1qtnf3 |
Chad |
Cspg4 |
H19 |
| Plagl1 |
Fbln7 |
Higd1a |
Pantr1 |
Mgp |
Bnip3 |
Rtl3 |
Sox5 |
Snhg18 |
Pcolce2 |
| Meltf |
Kdelr2 |
Cdkn1c |
Thbs1 |
Cytl1 |
Kazald1 |
P4ha2 |
Nfatc2 |
Cpe |
Maged2 |
| Epyc |
Ccn1 |
P3h3 |
Cd9 |
Ier3ip1 |
Fzd9 |
Rian |
S100a1 |
Meg3 |
Neat1 |
| Gpc3 |
Fos |
Sorbs2 |
Trim47 |
Fkbp11 |
Anxa5 |
Nrk |
Klf2 |
Sdc4 |
Mfge8 |
| Capn6 |
Slc26a2 |
Pxylp1 |
Rlbp1 |
Cd24a |
Sfrp1 |
Il17d |
Kcnq1ot1 |
Gdf10 |
Tmem97 |
| Scd2 |
Asb4 |
Pmp22 |
Dap |
Selenom |
Nid2 |
4930523C07Rik |
Scin |
Fosb |
Timp1 |
| Fmod |
Phlda2 |
Sparc |
Mest |
Crispld1 |
Fam180a |
Aldoc |
Phyh |
Ndufa4l2 |
Jund |
| Dhx58os |
Lgals3 |
Lbhd2 |
Egr1 |
Srm |
Scd1 |
Jun |
Nog |
Cgref1 |
Zbtb20 |
| Msmo1 |
Slc16a4 |
Fam162a |
Lmcd1 |
Cox4i2 |
Clec11a |
Ldhb |
Mif |
Ctsz |
Kcnk1 |
| Alkbh1 |
Bex2 |
Steap1 |
Pitx1 |
Ppa1 |
Pcolce |
Tspan8 |
Klf4 |
Vwc2 |
Emilin1 |
| Ndrg2 |
Moxd1 |
Tsc22d1 |
BC006965 |
Auts2 |
Serpina3n |
Ntm |
Tgfbi |
Ucma |
Tox |
| Tgm2 |
Hspa5 |
Lum |
Prkg2 |
Ebf1 |
Rgs3 |
Rgcc |
Foxc1 |
Fdps |
Tubb2a |
| Tbx15 |
Shox2 |
Dhcr7 |
Calml3 |
Klf9 |
Insig1 |
Timp3 |
Ier2 |
Hmgcr |
Pxdc1 |
| Angptl1 |
Enpp1 |
Id4 |
Tspan32 |
Bcl2 |
Col15a1 |
Srpx |
Cpm |
Tent5a |
Fam89a |
| Prss35 |
Ooep |
Klhl13 |
Lpar4 |
1700086L19Rik |
Saa1 |
Pdcd4 |
Chil1 |
Arl4a |
Ecrg4 |
| Fgfr2 |
Smoc2 |
Aebp1 |
Phlda1 |
Id3 |
Scn1b |
Hmga2 |
Gm26532 |
Hsp90b1 |
Uba7 |
| Lrig3 |
Ihh |
Bhlhe41 |
Dbi |
Bambi |
Cyp51 |
Idi1 |
Bmp2 |
Pam |
Trabd2b |
| Rspo3 |
Cox6b2 |
Ppp2r2b |
Smim5 |
Ddah1 |
Spsb4 |
Dusp14 |
H1f2 |
Nim1k |
Klk10 |
| Slc1a3 |
Lss |
Boc |
Cpxm1 |
Gem |
Bmper |
Saa2 |
2810403D21Rik |
Runx3 |
Smpd3 |
| Fabp7 |
Mgarp |
Foxc2 |
Cdc42ep3 |
Hmgcs1 |
Panx3 |
Runx2 |
Tshz2 |
Bmp5 |
Car9 |
| Acat2 |
Trib1 |
Smim14 |
Pth1r |
Sqle |
Enpp2 |
Aacs |
Mxra8 |
Clic4 |
2610528A11Rik |
| Efna1 |
Mvd |
Wfdc12 |
Tmem158 |
Zfp24 |
Barx1 |
Ung |
Tcf7l2 |
Serinc5 |
Htra3 |
| Sgms2 |
Cpq |
Id1 |
Fam81a |
Ppic |
Raph1 |
Id2 |
Ttll3 |
Fbp1 |
Ccn2 |
| Stk32b |
Smoc1 |
Arsi |
Mpz |
1700049E15Rik |
Psma8 |
Cd82 |
Cdh10 |
Pcp4 |
Cyp26b1 |
| Trps1 |
Ifitm5 |
Uts2b |
Batf3 |
3830403N18Rik |
Ltbp3 |
Ostn |
Angptl2 |
Efcab10 |
Tspan18 |
| Flrt2 |
Btbd3 |
Penk |
Pcsk6 |
Sema3d |
Cldn10 |
Dab2 |
Gng11 |
|
|
| GO:0061448 |
connective tissue development |
BP |
251 |
41 |
1.028e-24 |
| GO:0051216 |
cartilage development |
BP |
190 |
35 |
5.215e-23 |
| GO:0001501 |
skeletal system development |
BP |
465 |
51 |
2.159e-22 |
| GO:0001503 |
ossification |
BP |
330 |
43 |
7.132e-22 |
| GO:0030198 |
extracellular matrix organization |
BP |
229 |
35 |
3.146e-20 |
| GO:0009888 |
tissue development |
BP |
1556 |
90 |
1.978e-19 |
| GO:0043062 |
extracellular structure organization |
BP |
263 |
36 |
3.787e-19 |
| GO:0002062 |
chondrocyte differentiation |
BP |
114 |
25 |
1.19e-18 |
| GO:0048705 |
skeletal system morphogenesis |
BP |
235 |
31 |
3.709e-16 |
| GO:0048731 |
system development |
BP |
3637 |
142 |
8.386e-16 |
| Cdh5 |
Ecscr |
Icam2 |
Rasip1 |
Kdr |
Cldn5 |
Tie1 |
Pecam1 |
Myct1 |
Gimap6 |
| Emcn |
Kank3 |
Ctla2a |
Plvap |
Plxnd1 |
Cd93 |
Sox17 |
Gpihbp1 |
Aplnr |
Tmem255a |
| Grrp1 |
Robo4 |
Esam |
Cav1 |
Adgrl4 |
Lmo2 |
Col18a1 |
Thsd1 |
Npr1 |
Cd34 |
| Gimap4 |
Mfng |
Klhl4 |
Fam167b |
Eng |
Vamp5 |
Sox7 |
Rbpms |
Afap1l1 |
Flt1 |
| Arap3 |
Bik |
Gngt2 |
Adgrf5 |
Myzap |
C130074G19Rik |
Hlx |
Apold1 |
Ipo11 |
Tm4sf1 |
| Tek |
Fabp4 |
Dusp2 |
Smagp |
Prcp |
Arhgef15 |
Arhgap18 |
Cavin2 |
Clec1a |
Crmp1 |
| Flt4 |
Dok4 |
Ushbp1 |
Dll4 |
Ptprb |
Sipa1 |
Mmrn2 |
Gimap5 |
Scarf1 |
Grap |
| Madcam1 |
Ly6c1 |
Abi3 |
Crip2 |
Bok |
S100a16 |
Dysf |
N4bp3 |
Gimap1 |
Podxl |
| Acvrl1 |
Bcl6b |
F11r |
Pcdh17 |
Gm45837 |
Trp53i11 |
Mmp15 |
Lpar6 |
Tfpi |
Tspan15 |
| Tmem88 |
Cd36 |
Nova2 |
Apln |
Fgd5 |
Cav2 |
Gm16104 |
Sh3tc1 |
Cracr2b |
Cavin3 |
| Ehd2 |
BC028528 |
Prkch |
Fkbp1a |
Sult1a1 |
Gap43 |
Dusp3 |
Stab1 |
Adam15 |
Cmtm8 |
| Esm1 |
Cda |
C1qtnf9 |
Tpm4 |
Epas1 |
Ppp1r16b |
Mmrn1 |
Ets1 |
Pdgfb |
Ece1 |
| Fam43a |
Ccm2l |
Tnfaip8l1 |
Adm |
Hdac7 |
Cd38 |
Cdh13 |
Rftn1 |
Tmem252 |
Kcne3 |
| Gm13889 |
Tinagl1 |
Akap12 |
Npdc1 |
Ndrg1 |
Pcdh1 |
Procr |
Ccl21a |
Nectin2 |
Nrp2 |
| Kitl |
Gja4 |
Cngb1 |
Cd40 |
Hid1 |
Nts |
Gm525 |
Palmd |
Clec14a |
Lamb1 |
| Rasgrp2 |
Prrg3 |
Cavin1 |
Lck |
Kcna5 |
Ldb2 |
Calcrl |
Hoxb6 |
Mcam |
4930542C12Rik |
| Itga6 |
Arhgap31 |
Prox1 |
Lyve1 |
Reln |
Lama4 |
Zfp979 |
Hbegf |
Depp1 |
Ephb1 |
| Cd1d1 |
Arhgap29 |
Tcf15 |
Vwa1 |
Cotl1 |
Angpt2 |
F2r |
Slc16a13 |
Nedd9 |
Stc1 |
| Nr2f2 |
Enpp3 |
AU021092 |
Tc2n |
Sema6d |
Gpm6a |
Hoxd9 |
Ccl21d |
Arhgef28 |
Fam171a2 |
| Snrk |
Ifitm3 |
Meox2 |
Cpa1 |
Meox1 |
Filip1 |
Igf1 |
Hoxb9 |
Marchf11 |
Pgf |
| Fhl2 |
Edn1 |
Ccdc149 |
Apoa2 |
Myo1c |
Arhgef7 |
Cnrip1 |
Tspan12 |
Exoc3l4 |
Vwf |
| Tmod2 |
Lhx6 |
Adcy10 |
|
|
|
|
|
|
|
| GO:0001944 |
vasculature development |
BP |
646 |
56 |
7.329e-28 |
| GO:0072358 |
cardiovascular system development |
BP |
657 |
56 |
1.738e-27 |
| GO:0048514 |
blood vessel morphogenesis |
BP |
538 |
51 |
4.094e-27 |
| GO:0001568 |
blood vessel development |
BP |
617 |
54 |
4.934e-27 |
| GO:0001525 |
angiogenesis |
BP |
445 |
47 |
5.207e-27 |
| GO:0048646 |
anatomical structure formation involved in morphogenesis |
BP |
966 |
60 |
2.903e-22 |
| GO:0072359 |
circulatory system development |
BP |
971 |
60 |
3.777e-22 |
| GO:0035239 |
tube morphogenesis |
BP |
786 |
53 |
3.528e-21 |
| GO:2000145 |
regulation of cell motility |
BP |
800 |
51 |
2.685e-19 |
| GO:0040012 |
regulation of locomotion |
BP |
854 |
52 |
8.254e-19 |
options(backup.options)
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /scicore/soft/apps/OpenBLAS/0.3.1-GCC-7.3.0-2.30/lib/libopenblas_sandybridgep-r0.3.1.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Mm.eg.db_3.8.2 AnnotationDbi_1.46.1 IRanges_2.18.0
## [4] S4Vectors_0.22.1 Biobase_2.44.0 BiocGenerics_0.30.0
## [7] scWGCNA_0.1.0
##
## loaded via a namespace (and not attached):
## [1] backports_1.1.4 Hmisc_4.2-0 sn_1.6-1
## [4] plyr_1.8.4 igraph_1.2.4.1 lazyeval_0.2.2
## [7] splines_3.6.0 listenv_0.7.0 ggplot2_3.1.1
## [10] TH.data_1.0-10 robust_0.4-18 digest_0.6.25
## [13] foreach_1.4.4 htmltools_0.3.6 GO.db_3.8.2
## [16] gdata_2.18.0 magrittr_1.5 checkmate_1.9.3
## [19] memoise_1.1.0 fit.models_0.5-14 cluster_2.0.9
## [22] doParallel_1.0.14 ROCR_1.0-7 limma_3.40.0
## [25] sna_2.5 globals_0.12.4 fastcluster_1.1.25
## [28] matrixStats_0.54.0 sandwich_2.5-1 colorspace_1.4-1
## [31] rrcov_1.4-7 blob_1.1.1 ggrepel_0.8.2
## [34] xfun_0.20 dplyr_1.0.2 crayon_1.3.4
## [37] jsonlite_1.6.1 impute_1.58.0 survival_2.44-1.1
## [40] zoo_1.8-5 iterators_1.0.10 ape_5.3
## [43] glue_1.4.0 gtable_0.3.0 leiden_0.3.3
## [46] future.apply_1.2.0 DEoptimR_1.0-8 scales_1.0.0
## [49] mvtnorm_1.0-10 GGally_1.4.0 DBI_1.0.0
## [52] bibtex_0.4.2 Rcpp_1.0.1 metap_1.3
## [55] plotrix_3.7-5 viridisLite_0.3.0 htmlTable_1.13.1
## [58] reticulate_1.15 foreign_0.8-71 bit_1.1-14
## [61] rsvd_1.0.3 preprocessCore_1.46.0 Formula_1.2-3
## [64] tsne_0.1-3 htmlwidgets_1.3 httr_1.4.0
## [67] gplots_3.0.1.1 RColorBrewer_1.1-2 acepack_1.4.1
## [70] TFisher_0.2.0 ellipsis_0.3.0 Seurat_3.1.4
## [73] ica_1.0-2 reshape_0.8.8 pkgconfig_2.0.2
## [76] nnet_7.3-12 uwot_0.1.8 dynamicTreeCut_1.63-1
## [79] labeling_0.3 tidyselect_1.1.0 rlang_0.4.7
## [82] reshape2_1.4.3 munsell_0.5.0 tools_3.6.0
## [85] generics_0.0.2 RSQLite_2.1.1 statnet.common_4.3.0
## [88] ggridges_0.5.1 evaluate_0.13 stringr_1.4.0
## [91] yaml_2.2.0 npsurv_0.4-0 knitr_1.22
## [94] bit64_0.9-7 fitdistrplus_1.0-14 robustbase_0.93-5
## [97] caTools_1.17.1.2 purrr_0.3.4 RANN_2.6.1
## [100] pbapply_1.4-1 future_1.13.0 nlme_3.1-140
## [103] compiler_3.6.0 rstudioapi_0.11 plotly_4.9.2.1
## [106] png_0.1-7 lsei_1.2-0 tibble_3.0.3
## [109] pcaPP_1.9-73 stringi_1.4.3 highr_0.8
## [112] lattice_0.20-38 Matrix_1.2-17 multtest_2.38.0
## [115] vctrs_0.3.4 mutoss_0.1-12 pillar_1.4.6
## [118] lifecycle_0.2.0 Rdpack_0.11-0 lmtest_0.9-37
## [121] RcppAnnoy_0.0.16 data.table_1.12.2 cowplot_1.0.0
## [124] bitops_1.0-6 irlba_2.3.3 gbRd_0.4-11
## [127] patchwork_1.0.0 R6_2.4.0 latticeExtra_0.6-28
## [130] network_1.16.0 KernSmooth_2.23-15 gridExtra_2.3
## [133] codetools_0.2-16 MASS_7.3-51.4 gtools_3.8.1
## [136] sctransform_0.2.1 mnormt_1.5-5 multcomp_1.4-10
## [139] grid_3.6.0 rpart_4.1-15 coda_0.19-2
## [142] tidyr_1.1.2 rmarkdown_2.6 Rtsne_0.15
## [145] numDeriv_2016.8-1 WGCNA_1.67 base64enc_0.1-3